District Wise Price Forecasting of Wheat in Pakistan using Deep Learning

Abstract

Wheat is the main agricultural crop of Pakistan and is a staple food requirement of almost every Pakistani household making it the main strategic commodity of the country whose availability and affordability is the government's main priority. Wheat food availability can be vastly affected by multiple factors included but not limited to the production, consumption, financial crisis, inflation, or volatile market. The government ensures food security by particular policy and monitory arrangements, which keeps up purchase parity for the poor. Such arrangements can be made more effective if a dynamic analysis is carried out to estimate the future yield based on certain current factors. Future planning of commodity pricing is achievable by forecasting their future price anticipated by the current circumstances. This paper presents a wheat price forecasting methodology, which uses the price, weather, production, and consumption trends for wheat prices taken over the past few years and analyzes them with the help of advance neural networks architecture Long Short Term Memory (LSTM) networks. The proposed methodology presented significantly improved results versus other conventional machine learning and statistical time series analysis methods. INDEX TERMSTime-series analysis, Machine learning, Economic forecasting, Artificial neural networks VOLUME 0, 0000

Wholesale price forecasts of green grams using the neural network

“…In recent times, there has been a great deal of interest among researchers to investigate the suitability of machine learning methods for commodity price forecasts. Consequently, studies encompassing an extensive array of agricultural commodities have been carried out in response to the increased accessibility of computer resources and tools (Yuan et al, 2020;RL and Mishra, 2021;Bayona-Or e et al, 2021;Storm et al, 2020;Kouadio et al, 2018;Abreham, 2019;Huy et al, 2019;Degife and Sinamo, 2019;Naveena and Subedar, 2017;Lopes, 2018;Mayabi, 2019;Moreno and Salazar, 2018;Zelingher et al, 2021;Shahhosseini et al, 2021Shahhosseini et al, , 2020dos Reis Filho et al, 2020;Zelingher et al, 2020;Ribeiro et al, 2019;Surjandari et al, 2015;Ayankoya et al, 2016;Ali et al, 2018;Fang et al, 2020;Harris, 2017;Li et al, 2022;Yoosefzadeh-Najafabadi et al, 2021;Ribeiro and dos Santos Coelho, 2020;Zhao, 2021;Jiang et al, 2019;Handoyo and Chen, 2020;Silalahi, 2013;Li et al, 2020;Ribeiro and Oliveira, 2011;Zhang et al, 2021;Melo et al, 2007;de Melo et al, 2004;Kohzadi et al, 1996;Zou et al, 2007;Rasheed et al, 2021;…”

Yellow corn wholesale price forecasts via the neural network

“…Some typical models sought in previous studies include the ARIMA model, VAR model and VECM model. Over the past decade, computational power has becoming much more affordable, and the interest among researchers in building machine learning models aiming at offering good forecasts in economics and finance has been well documented (Ge, Jiang, He, Zhu, & Zhang, 2020;Yang & Wang, 2019), including, of course, forecasts of commodity prices for the agricultural market (Abreham, 2019;Ali, Deo, Downs, & Maraseni, 2018;Antwi, Gyamfi, Kyei, Gill, & Adam, 2022;Ayankoya, Calitz, & Greyling, 2016;Degife & Sinamo, 2019;Deina et al, 2021;Dias & Rocha, 2019;Fang, Guan, Wu, & Heravi, 2020;Filippi et al, 2019;G omez, Salvador, Sanz, & Casanova, 2021;Handoyo & Chen, 2020;Harris, 2017;Huy, Thac, Thu, Nhat, & Ngoc, 2019;Jiang, He, & Zeng, 2019;Khamis & Abdullah, 2014;Kohzadi, Boyd, Kermanshahi, & Kaastra, 1996;Kouadio et al, 2018;Li, Chen, Li, Wang, & Xu, 2020, Li, Li, Liu, Zhu, & Wei, 2020Lopes, 2018;Mayabi, 2019;de Melo, J unior, & Milioni, 2004;Melo, Milioni, & Nascimento J unior, 2007;Moreno et al, 2018;Naveena et al, 2017;Rasheed, Younis, Ahmad, Qadir, & Kashif, 2021;dos Reis Filho, Correa, Freire, & Rezende, 2020;Ribeiro & Oliveira, 2011;Ribeiro, Ribeiro, Reynoso-Meza, & dos Santos Coelho, 2019;Ribeiro & dos Santos Coelho, 2020;RL & Mishra, 2021;…”

Scrap steel price forecasting with neural networks for east, north, south, central, northeast, and southwest China and at the national level

“…In recent years, as computing resources and tools are becoming continuously easier and more affordable to reach [100], researchers across the globe have started to show continuously increasing interest in exploring applications of machine learning techniques [101] for the purpose of forecasting commodity prices. Corresponding studies in the literature have covered many different commodities from different economic sectors and industries, including but not limited to those in the agricultural sector, such as soybeans [102][103][104][105][106][107][108], soybean oil [109][110][111], palm oil [112], sugar [113][114][115][116][117][118], corn [102,113,[119][120][121][122][123][124][125][126][127][128][129][130][131], wheat [105,[132][133][134][135][136][137][138][139], coffee [140][141]…”